Xianshan Li
Papers
1
Total Citations
4
H-Index
1
About
Xianshan Li is a researcher at the forefront of human-robot interaction, specializing in natural language processing for medical service robotics. Her most-cited work, "Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots" (2021, 4 citations), introduces a novel deep reinforcement learning approach—using Deep Q-Networks (DQN)—to bridge the gap between verbal commands and robotic actions. This contribution addresses a critical challenge in medical robotics: enabling robots to reliably interpret and execute complex, context-dependent instructions from doctors, thereby enhancing surgical assistance and patient care. Li’s research focuses on designing simple yet stable interaction systems that reduce cognitive load on medical professionals while improving operational precision. While her citation count reflects an emerging career, her work is foundational for advancing autonomous medical robots in real-world clinical settings. By integrating reinforcement learning with semantic parsing, Li is paving the way for more intuitive and adaptive robotic assistants, a key step toward seamless human-robot collaboration in healthcare.
Research Focus
Key Achievements
Top Papers
- 1